DESCRIPTION: The long-term goal of this research is the prevention of unintentional injuries due to vehicle crashes by older drivers, through risk factor identification and the development and evaluation of interventions to minimize risk. The objective of the proposed study is to identify performance limitations which place older drivers at risk for crashes in which the older driver is injured. Special attention will be directed at visual and cognitive (including attentional) performance since these skills are critical for vehicle control, tend to decline with age yet are under certain circumstances reversible, and have been associated with unsafe driving among the elderly in prior research. This objective will be addressed by a multiple cohort follow-up study utilizing polled analysis techniques, on an aggregate sample of 3,839 older drivers brought together through the University of Alabama at Birmingham's Center for Research in Applied Gerontology. Visual, cognitive, and medical data are already being collected on these drivers through other studies and is thus financially underwritten by other projects. A major advantage of the research design is that the high cost of collecting functional data on almost 4,000 older drivers does not have to be covered by this project. In addition, the cost of procuring crash data (including the detailed accident report) is also financially underwritten by five or six cohort sites. Injury information will be collected from medical records from the treatment facility, using state-of-the-art injury coding schemes. The cohorts proposed for study will have up to 9 years of follow-up. An interdiscliplinary research team has been assembled to carry out this study, including researchers expert in vision and cognitive sciences, older driver research, injury epidemiology, medicine, gerontology, and biostatistics. The main hypothesis to be tested is that the visual/cognitive impairments that place older drivers at risk for crashes, also place them at risk for injury. However, crashes that lead to injury may be more strongly related to more severe forms of visual/cognitive impairment, than crashes that do not lead to injury. Older drivers in poor health are more likely to incur crashes than those in good health, where poor health is defined as the existence of co-morbid medical conditions. Data analysis will center around developing predictive models of injurious crashes based on visual and cognitive function and medical conditions.